TemporalGeneralizedRules 1.0.0

Temporal Generalized Association Rules

This library provides four algorithms related to Association Rule mining. The algorithms are:

• vertical_apriori
• vertical_cumulate
• htar
• htgar

These algorithms use a transactional dataset that is transformed to a vertical format for optimization. Dataset MUST follow the following format:

Or if timestamps are provided:

Each field is separated by ","

TGAR

This is the main class that must be instantiated once.

Usage

import TemporalGeneralizedRules

tgar = TemporalGeneralizedRules.TGAR()

Vertical Apriori

This algorithm has four parameters:

• filepath: Filepath of the dataset in csv format with the format discussed in the previous section.
• min_supp: Minimum support threshold.
• min_conf: Minimum confidence threshold.
• parallel_count: Optional parameter that enables parallelization in candidate count phase of the algorithm.

Usage

tgar.apriori("dataset.csv", 0.05, 0.5)

Vertical Cumulate

This algorithm has five parameters:

• filepath: Filepath of the dataset in csv format with the format discussed in the previous section.
• min_supp: Minimum support threshold.
• min_conf: Minimum confidence threshold.
• min_r: Minimum R-interesting threshold.
• parallel_count: Optional parameter that enables parallelization in candidate count phase of the algorithm. It can make execution faster.

Usage

tgar.vertical_cumulate("dataset.csv", 0.05, 0.5, 1.1)

HTAR

This algorithm has four parameters:

• filepath: Filepath of the dataset in csv format with the format discussed in the previous section.
• min_supp: Minimum support threshold.
• min_conf: Minimum confidence threshold.
• parallel_count: Optional parameter that enables parallelization in candidate count phase of the algorithm. It can make execution faster.

Usage

tgar.htar("dataset.csv", 0.05, 0.5)

HTGAR

This algorithm has five parameters:

• filepath: Filepath of the dataset in csv format with the format discussed in the previous section.
• min_supp: Minimum support threshold.
• min_conf: Minimum confidence threshold.
• min_r: Minimum R-interesting threshold.
• parallel_count: Optional parameter that enables parallelization in candidate count phase of the algorithm. It can make execution faster.

Usage

tgar.htgar("dataset.csv", 0.05, 0.5, 1.1)

Pypy

For a better performance we recommend using this package with Pypy, a faster implementation of python.

https://www.pypy.org/

Bibliography

The following were based on the following papers:

• Apriori : https://dl.acm.org/doi/10.5555/645920.672836
• Cumulate: https://www.sciencedirect.com/science/article/pii/S0167739X97000198
• HTAR: https://www.sciencedirect.com/science/article/pii/S2210832716000041